The COVID-19 pandemic caused educational institutions to close for the safety of students and staff and to aid in prevention measures around the world to slow the spread of the outbreak. Closures of schools and the interruption of education affected billions of enrolled students of all ages, leading to nearly the entire student population to be impacted by these measures. Consequently, this changed the educational landscape. Emergency remote education (ERE) was put into practice to ensure the continuity of education and caused the need to reinterpret pedagogical approaches. The crisis revealed flaws within our education systems and exemplified how unprepared schools were for the educational crisis both in K-12 and higher education contexts. These shortcomings require further research on education and emerging pedagogies for the future. The Handbook of Research on Emerging Pedagogies for the Future of Education: Trauma-Informed, Care, and Pandemic Pedagogy evaluates the interruption of education, reports best-practices, identifies the strengths and weaknesses of educational systems, and provides a base for emerging pedagogies. The book provides an overview of education in the new normal by distilling lessons learned and extracting the knowledge and experience gained through the COVID-19 global crisis to better envision the emerging pedagogies for the future of education. The chapters cover various subjects that include mathematics, English, science, and medical education, and span all schooling levels from preschool to higher education. The target audience of this book will be composed of professionals, researchers, instructional designers, decision-makers, institutions, and most importantly, main-actors from the educational landscape interested in interpreting the emerging pedagogies and future of education due to the pandemic.

Artificial intelligence (AI) and knowledge management can create innovative digital solutions and business opportunities in Asia from circular and green economies to technological disruption, innovation, and smart cities. It is essential to understand the impact and importance of AI and knowledge management within the digital economy for future development and for fostering the best practices within 21st century businesses. The Handbook of Research on Artificial Intelligence and Knowledge Management in Asia's Digital Economy offers conceptual frameworks, empirical studies, and case studies that help to understand the latest developments in artificial intelligence and knowledge management, as well as its potential for digital transformation and business opportunities in Asia. Covering topics such as augmented reality. Convolutional neural networks, and digital transformation, this major reference work generates enriching debate on the challenges and opportunities for economic growth and inclusion in the region among business executives and leaders, IT managers, policymakers, government officials, students and educators of higher education, researchers, and academicians.

This is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces.

From parsing to indexing, clustering to classification, retrieval to ranking, and user feedback to retrieval evaluation, all of the most important concepts are carefully introduced and exemplified. The contents and structure of the book have been carefully designed by the two main authors, with individual contributions coming from leading international authorities in the field, including Yoelle Maarek, Senior Director of Yahoo Research Israel; Dulce Ponceleon IBM Research; and Malcolm Slaney, Yahoo Research USA.

This completely reorganized, revised and enlarged second edition of "Modern Information Retrieval" contains many new chapters and double the number of pages and bibliographic references of the first edition, and a companion website www.mir2ed.org with teaching material. It will prove invaluable to students, professors, researchers, practitioners, and scholars of this fascinating field of information retrieval.

The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.

Technology is used in various forms within today’s modern market. Businesses and companies, specifically, are beginning to manage their effectiveness and performance using intelligent systems and other modes of digitization. The rise of artificial intelligence and automation has caused organizations to re-examine how they utilize their personnel and how to train employees for new skillsets using these technologies. These responsibilities fall on the shoulders of human resources, creating a need for further understanding of autonomous systems and their capabilities within organizational progression. Transforming Human Resource Functions With Automation is a collection of innovative research on the methods and applications of artificial intelligence and autonomous systems within human resource management and modern alterations that are occurring. While highlighting topics including cloud-based systems, robotics, and social media, this book is ideally designed for managers, practitioners, researchers, executives, policymakers, strategists, academicians, and students seeking current research on advancements within human resource strategies through the implementation of information technology and automation.

Now in its fifth edition, bridges the gap between the technical specifications and the real world of designing and programming devices that connect over the Universal Serial Bus (USB). Readers will learn how to select the appropriate USB speed, device class, and hardware for a device; communicate with devices using Visual C# and Visual Basic; use standard host drivers to access devices, including devices that perform vendor-defined tasks; save power with USB's built-in power-conserving protocols; and create robust designs using testing and debugging tools. This fully revised edition also includes instruction on how to increase bus speed with SuperSpeed and SuperSpeedPlus, implement wireless communications, and develop for USB On-The-Go and embedded hosts.

Due to the growing prevalence of artificial intelligence technologies, schools, museums, and art galleries will need to change traditional ways of working and conventional thought processes to fully embrace their potential. Integrating virtual and augmented reality technologies and wearable devices into these fields can promote higher engagement in an increasingly digital world. Virtual and Augmented Reality in Education, Art, and Museums is an essential research book that explores the strategic role and use of virtual and augmented reality in shaping visitor experiences at art galleries and museums and their ability to enhance education. Highlighting a range of topics such as online learning, digital heritage, and gaming, this book is ideal for museum directors, tour developers, educational software designers, 3D artists, designers, curators, preservationists, conservationists, education coordinators, academicians, researchers, and students.

There is no doubt that there has been much excitement regarding the pioneering contributions of artificial intelligence (AI), the internet of things (IoT), and blockchain technologies and tools in visualizing and realizing smarter as well as sophisticated systems and services. However, researchers are being bombarded with various machine and deep learning algorithms, which are categorized as a part and parcel of the enigmatic AI discipline. The knowledge discovered gets disseminated to actuators and other concerned systems in order to empower them to intelligently plan and insightfully execute appropriate tasks with clarity and confidence. The IoT processes in conjunction with the AI algorithms and blockchain technology are bound to lay out a stimulating foundation for producing and sustaining smarter systems for society. The Handbook of Research on Smarter and Secure Industrial Applications Using AI, IoT, and Blockchain Technology articulates and accentuates various AI algorithms, fresh innovations in the IoT, and blockchain spaces. The domain of transforming raw data to information and to relevant knowledge is gaining prominence with the availability of data ingestion, processing, mining, analytics algorithms, platforms, frameworks, and other accelerators. Covering topics such as blockchain applications, Industry 4.0, and cryptography, this book serves as a comprehensive guide for AI researchers, faculty members, IT professionals, academicians, students, researchers, and industry professionals.

This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms.Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to thestatistical approach to the analysis of complex networks.In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition "a la carte". Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.